Application of artificial bee colony-based optimization for fault section estimation in power systems

This paper presents an artificial bee colony algorithm to enhance the fault section estimation performance in power systems. Through mimicking the foraging behaviors of honeybee swarms, the algorithm owns exploitation and exploration procedures to constitute an effective near-optimal search mechanism. This proposed method excels at saving decision on external parameters such as crossover and mutation rates, facilitating the improvement of computation performance. Meanwhile, the method has added a random selection scheme to look for a new source, by which the probability of being trapped into local minimum can be largely reduced, hence serving as beneficial aids of grasping the faulted section more effectively. Through this proposed approach, it benefits the engineers to find the accurate fault section among voluminous alarms, and reduces the possibility of inaccurate diagnosis. To validate the effectiveness of the method, it has been tested on practical power systems with comparisons to published techniques.

► An artificial bee colony algorithm is employed to solve the fault section estimation problem in power systems. ► Both the failures of protective devices and multiple fault disturbances are considered. ► The results of the proposed method show that it is applicable to a large-scale power system. ► The proposed method framework can be used as an efficient optimizer for fault section estimation problems.